Nuclear Density Functional Theory (DFT): perspectives and ab initio-based functionals

Nuclear Density Functional Theory (DFT) is widely used to predict global properties of nuclei throughout the isotope chart. Despite many successes, it is not obvious how to make further progress and improve the predictive power of this theory. Among the different attempts, in this contribution we discuss our investigation on constructing nuclear Energy Density Functionals (EDFs) based on the \textit{ab initio} nuclear theory.

Authors

Publication Details

Published
2025-02-07
DOI
https://doi.org/10.22323/1.465.0200
Primary Topic
thermodynamics and calorimetric analyses
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Nuclear Density Functional Theory (DFT): perspectives and ab initio-based functionals

Alessandro Lovato, Francesco Marino, G. Colò, C. Barbieri et al.
thermodynamics and calorimetric analyses
article

Nuclear Density Functional Theory (DFT): perspectives and ab initio-based functionals

Alessandro Lovato, Francesco Marino, G. Colò, C. Barbieri, Francesco Pederiva
article en

Abstract

Nuclear Density Functional Theory (DFT) is widely used to predict global properties of nuclei throughout the isotope chart. Despite many successes, it is not obvious how to make further progress and improve the predictive power of this theory. Among the different attempts, in this contribution we discuss our investigation on constructing nuclear Energy Density Functionals (EDFs) based on the \textit{ab initio} nuclear theory.

Openalex Percentile: Top 97%
thermodynamics and calorimetric analyses
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.